Multiple sclerosis (MS) is a genetic disease caused by demyelination of nerve tissue, which leads to cognitive deficits, balance and coordination impairment, pain and numbness, amongst other clinical presentation. MS is the prototypic human demyelinating disease with evidence from numerous epidemiologic, adoption and twin studies for a strong underlying genetic liability (Wilier et al. (2003)Proc. Natl. Acad. Sci. U.S.A 100:12877-12882). The disease is most common in young adults, with more than 90% of patients diagnosed before the age of 55 and less than 5% before the age of 14. Females are 2-3 times more frequently affected than males (Fernald et al. (2005) J. Neuroimmunol. 167:157-169) and the disease course can vary substantially with some patients suffering only minor disability several decades after their initial diagnosis, and others reaching wheelchair dependency shortly after disease onset.
To identify genetic contributions that underlie the development and progression of MS, several different approaches, including genetic linkage, candidate gene association and gene expression studies have been independently employed (Fernald et al. (2005) J. Neuroimmunol. 167:157-169). However, the genetic linkage screens have failed to consistently identify consensus regions outside of the HLA class II locus. Candidate gene studies have suggested over 100 different associated genes, but none have reached consensus. Similarly, gene expression studies have identified hundreds of differentially expressed transcripts with little consistency across studies.
The present invention overcomes previous shortcomings in the art by employing genomic convergence (Hauser et al. (2003) Hum. Mol. Genet. 12:671-677), which takes advantage of the strengths of each method by combining evidence from both statistical and functional data, to identify significant statistical associations between nucleotide variants within the interleukin 7 receptor alpha (IL7Rα) chain gene and MS.